From e9bac094277fc3f06445d6e2bff7c810343289b9 Mon Sep 17 00:00:00 2001
From: Morten Hjorth-Jensen
This quantity defines was what is called the Hessian matrix (the second derivative of a function we want to optimize).
diff --git a/doc/pub/week35/html/._week35-bs058.html b/doc/pub/week35/html/._week35-bs058.html index f52640fb5..b6bab24dc 100644 --- a/doc/pub/week35/html/._week35-bs058.html +++ b/doc/pub/week35/html/._week35-bs058.html @@ -405,7 +405,7 @@ with a factor \( 1/(n-1) \). This is called Scikit-Learn or nunmpy's function calculate the covariance, this +Scikit-Learn or nunmpy's function to calculate the covariance, this quantity will be computed with a factor \( 1/(n-1) \). diff --git a/doc/pub/week35/html/week35-solarized.html b/doc/pub/week35/html/week35-solarized.html index 43267a8e9..0f1fa52af 100644 --- a/doc/pub/week35/html/week35-solarized.html +++ b/doc/pub/week35/html/week35-solarized.html @@ -2823,7 +2823,7 @@ function, that is we have $$ -\frac{\partial^2 C(\boldsymbol{\beta})}{\partial \boldsymbol{\beta}^T\partial \boldsymbol{\beta}} =\frac{2}{n}\boldsymbol{X}^T\boldsymbol{X}. +\frac{\partial^2 C(\boldsymbol{\beta})}{\partial \boldsymbol{\beta}\partial \boldsymbol{\beta}^T} =\frac{2}{n}\boldsymbol{X}^T\boldsymbol{X}. $$This quantity defines was what is called the Hessian matrix (the second derivative of a function we want to optimize).
@@ -2879,7 +2879,7 @@ with a factor \( 1/(n-1) \). This is called Scikit-Learn or nunmpy's function calculate the covariance, this +Scikit-Learn or nunmpy's function to calculate the covariance, this quantity will be computed with a factor \( 1/(n-1) \). diff --git a/doc/pub/week35/ipynb/ipynb-week35-src.tar.gz b/doc/pub/week35/ipynb/ipynb-week35-src.tar.gz index 6d899cd1864df13805929656c2a04890f610b4f7..75552ed8f53cb5dc116f18a7f25bb4b9f6777d1d 100644 GIT binary patch literal 191 zcmV;w06_mAiwFQw6YyjJ1MSaC3c@fD2H>uHia9|^+N50zx^N+gc!88oZLCddlA^u6 zeSoeMH${Yen}0%vVdk(|t#_Nq-CeL4LP*LOjG3i;N)pfS38fq;V;LK(m;#{*kK+Ky zax1-b)(JDL(o|